language:
- en
- id
- ta
- th
- vi
license: llama3
LLaMA3 8B SEA-LIONv2
SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. This model is continued pre-trained from the Meta-Llama-3-8B-Instruct model. This is the card for the LLaMA3 8B SEA-LIONv2 base model.
SEA-LION stands for Southeast Asian Languages In One Network.
Model Details
Model Description
The LLaMA3 8B SEA-LIONv model is a significant leap forward in the field of Natural Language Processing, specifically trained to understand the SEA regional context.
For tokenization, the model employs the default tokenizer used in Meta-Llama-3-8B-Instruct.
The continued pre-training data for LLaMA3 8B SEA-LIONv2 base model encompasses approximately 48B tokens.
- Developed by: Products Pillar, AI Singapore
- Funded by: Singapore NRF
- Model type: Decoder
- Languages: English, Indonesian, Thai, Vietnamese, Tamil
- License: LLaMA3 Community License
Performance Benchmarks
LLaMA3 8B SEA-LIONv has a similar English performance with LLaMA3-8B-Base model:
| Model | ARC | BBH | HellaSwag | MMLU | GSM8k | Average |
|---|---|---|---|---|---|---|
| LLaMA3 8B SEA-LIONv2 | 58.87 | 47.70 | 81.14 | 63.11 | 50.49 | 60.26 |
Training Details
Data
LLaMA3 8B SEA-LIONv2 base model was continued pre-trained on 48B tokens of the following data:
| Data Source | Unique Tokens | Multiplier | Total Tokens | Percentage |
|---|---|---|---|---|
| Dolma RefinedWeb - English | 7.650B | 1 | 7.650B | 15.90% |
| Dolma C4 - English | 1.160B | 1 | 1B | 9.21% |
| Dolma Reddit - English | 1.339B | 1 | 14.7B | 2.42% |
| Dolma Semantic Scholar | 0.959B | 1 | 2.9B | 2.79% |
| Dolma arXiv | 0.469B | 1 | 5.3B | 1.99% |
| Dolma StarCoder | 4.422B | 1 | 4.9B | 0.98% |
| SEA-LION Pile - Indonesian | 3.4B | 1 | 6.8B | 14.17% |
| Wiki* - Indonesian | 0.3B | 4 | 1.2B | 2.50% |
| SEA-LION Pile - Tamil | 5.6B | 1 | 5.6B | 11.67% |
| Wiki* + News - Tamil | 0.6B | 4 | 2.4B | 5.00% |
| SEA-LION Pile - Thai | 2.28B | 1 | 2.28B | 4.75% |
| WangChanBERTa - Thai | 5B | 1 | 5B | 10.42% |
| Wiki* - Thai | 0.18B | 4 | 0.72B | 1.50% |
| SEA-LION Pile - Vietnamese | 6.76B | 1 | 6.76B | 14.08% |
| Wiki* - Vietnamese | 0.31B | 4 | 1.24B | 2.58% |
Note:
- All token counts are counted using LLaMA3 tokenizer
- wiki* sources includes Wikipedia, Wiki Books, Wiki Source and Wiki Voyage
- Source of Tamil news is source with permission from Seithi
Infrastructure
LLaMA3 8B SEA-LIONv2 was trained using MosaicML Composer on the following hardware:
| Training Details | LLaMA3 8B SEA-LIONv2 |
|---|---|
| AWS EC2 p5d.24xlarge | 8 instances |
| Nvidia H100 80GB GPU | 64 |
| Training Duration | 2 days |
Configuration
| HyperParameter | LLaMA3 8B SEA-LIONv2 |
|---|---|
| Precision | bfloat16 |
| Optimizer | decoupled_adamw |
| Scheduler | weight_stable_decay |
| Learning Rate | 1.0e-5 |
| Global Batch Size | 512 |
| Micro Batch Size | 2 |
The Team
Brandon Ong
Bryan Siow
Esther Choa
Huang Yuli
Lee Chwan Ren
Leong Wai Yi
Leong Wei Qi
Li Yier
Liu Bing Jie Darius
Lovenia Holy
Montalan Jann Railey
Ng Boon Cheong Raymond
Ngui Jian Gang
Nguyen Thanh Ngan
Nicholas Cheng
Ong Tat-Wee David
Ong Zhi Hao
Rengarajan Hamsawardhini
Susanto Yosephine
Tai Ngee Chia
Tan Choon Meng
Teo Jin Howe
Teo Eng Sipp Leslie
Teo Wei Yi
Tjhi William
Walter Teng
Wayne Lau
Yeo Yeow Tong
Yong Xianbin
Acknowledgements
AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
Contact
For more info, please contact us using this SEA-LION Inquiry Form
Link to SEA-LION's GitHub repository
Disclaimer
This the repository for the base model. The model has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes.
References
@misc{lowphansirikul2021wangchanberta,
title={WangchanBERTa: Pretraining transformer-based Thai Language Models},
author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong},
year={2021},
eprint={2101.09635},
archivePrefix={arXiv},
primaryClass={cs.CL}
}